Y: = 1.0+0.5y; –i+e, €z ~ iidN(0,1) Yt = Yt– 1+€4» e, ~ iidN(0.5,1)
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
The AR model is a representative stable time series model and the random walk model is a representative unstable time series model. When the AR model and the arbitrary walking model are given as follows, the mean and variance are calculated and (approximately) stabilized time series model or unstable time series model is used by means and variances.
Explain why
Autoregressive model is a representation of a type of random process as such it is used to describe certain time varying processes in nature. The autoregressive model specifies that the output variable depends linearly on its own previous values and on a stochastic term.
Step by step
Solved in 2 steps
